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Gartner recently published its 2019 Magic Quadrant for Data Science and Machine Learning Platforms. You can secure a copy of the report from Gartner if you are a client, or read it for free here, courtesy of DataRobot. (Registration required.) Here’s how Gartner positioned the 17 vendors that made it into the MQ: Back in October, I wrote my predictions for

The die is cast. Last month, Gartner selected 16 vendors to include in its 2019 Magic Quadrant for Data Science and Machine Learning. Now, as Gartner prepares to publish the report early next year, I think it will be fun to make some predictions about how each vendor will fare. Some ground rules. I’m not going to talk about DataRobot,

TechCrunch reports results of a new study, which says that you really don’t need a co-founder after all. Next, they’ll be telling us we don’t need to floss. Python and R Matt Asay argues that Python is a gateway language that leads data scientists to R. (h/t Oliver Vagner). That’s oversimplified and mostly incorrect. The breadth of R’s analytics functionality tends

In hard news this week, Storm hits a milestone with Release 1.0, Google releases TensorFlow 0.8 with distributed computing support, and DataStax announces DataStax Enterprise Graph. And, following on NVIDIA’s DGX-1 announcement last week there are a number of items on Deep Learning featured below. Deep Learning — Adrian Colyer summarizes a paper that summarizes 900 other papers on Deep Learning. —

Microsoft’s chatbot fail wins the internet this week, but the most important story is Google’s new managed service for machine learning. Also leading the week: Mesosphere’s new funding round led by Microsoft and HPE, and more funding for Domo. — Google Cloud Platform (GCP) adds the Google Cloud Machine Learning Platform to its suite of managed machine learning services, which